Online mutual support communities provide vital spaces for individuals to seek guidance, share experiences, and engage in collective discourse. This study examines engagement dynamics within Douban's Women in Academia group, a digital support community for women in higher education. Using a large-scale dataset spanning over three years, this research employs Latent Dirichlet Allocation (LDA) to identify prevalent discussion topics and utilizes machine learning modeling to investigate factors influencing post engagement. The findings reveal that discussions primarily center around research advice, emotional expression, and feminist discourse, aligning with broader literature on online social support and gendered digital engagement. The results highlight that historical user activity, emotional intensity, and participatory behaviors strongly predict engagement levels. Additionally, interaction effects suggest that emotional expression plays a particularly significant role in feminist discussions. These insights contribute to the understanding of digital support mechanisms and provide practical recommendations for optimizing engagement strategies in online feminist and academic communities.
Y WANG (Sun,) studied this question.